Skip to main content
SpringerLink
Log in

Unconstrained Ordination

  • Chapter
  • First Online:
Numerical Ecology with R

Part of the book series: Use R ((USE R))

Abstract

While cluster analysis looks for discontinuities in a dataset, ordination extracts the main trends in the form of continuous axes. It is therefore particularly well adapted to analyse data from natural ecological communities, which are generally structured in gradients. Practically, you will: Learn how to choose among various ordination techniques (PCA, CA, PCoA and NMDS), compute them using the correct options, and properly interpret the ordination diagrams Apply these techniques to the Doubs river data Overlay the result of a cluster analysis on an ordination diagram to improve the interpretation of the clustering results Interpret the structures in the species data using the environmental variables from a second dataset Write your own PCA function

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Notes

  1. 1.

     Comparison of a PCA result with the broken stick model can also be done by using function PCAsignificance() of package BiodiversityR.

  2. 2.

     Note, however, that vegan uses an internal constant to rescale its results, so that the vectors and the circle represented here are not equal but proportional to their original values. See the code of the cleanplot.pca( ) function.

References

  • Gower, J. C. and Legendre, P.: Metric and Euclidean properties of dissimilarity coefficients. Journal of Classification 3, 5–48 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  • Legendre, P. and Gallagher, E. D.: Ecologically meaningful transformations for ordination of species data. Oecologia 129, 271–280 (2001)

    Article  Google Scholar 

  • Legendre, P. and Legendre, L.: Numerical Ecology. 2nd English edition. Elsevier, Amsterdam (1998)

    Google Scholar 

  • ter Braak, C. J. F and Smilauer, P.: CANOCO reference manual and CanoDraw for Windows user’s guide: software for canonical community ordination (ver. 4.5). Microcomputer Power, New York (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Département de sciences biologiques, Université de Montréal, C.P. 6128, succursale Centre-ville, Montréal, Québec, H3C 3J7, Canada

    Daniel Borcard & Pierre Legendre

  2. UMR 6249 Chrono-environnement UFR Sciences et Techniques, Université de Franche-Comté - CNRS, 16, Route de Gray, F-25030, Besançon cedex, France

    François Gillet

Authors
  1. Daniel Borcard
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. François Gillet
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pierre Legendre
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel Borcard .

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Borcard, D., Gillet, F., Legendre, P. (2011). Unconstrained Ordination. In: Numerical Ecology with R. Use R. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7976-6_5

Download citation

Publish with us

Policies and ethics

Search

Navigation